SVM are known to be difficult to grasp. Many people refer to them as "black box".
This tutorial series is intended to give you all the necessary tools to really understand the math behind SVM.
It starts softly and then get more complicated. But my goal here is to keep everybody on board, especially people who do not have a strong mathematical background.

SVM R tutorials

R is a good language if you want to experiment with SVM.
So I wrote some introductory tutorials about it.
The article about Support Vector Regression might interest you even if you don't use R.

Text classification tutorials

SVM can be applied to a wide variety of subjects. One of them is text classification. In the following tutorials you will learn how to transform text into data that you can feed to your SVM.
You will then see how to use this data to perform text classification (in R or in C#)